Content analysis is to words (and other unstructured data) as statistics is to numbers (also called structured data)—an umbrella term encompassing a range of analytic techniques. Content analyses range from purely qualitative analyses, often used in grounded theorizing and case-based research to reduce interview data into theoretically meaningful categories, to highly quantitative analyses that use concept dictionaries to convert words and phrases into numerical tables for further quantitative analysis. Common specialized types of qualitative content analysis include methods associated with grounded theorizing, narrative analysis, discourse analysis, rhetorical analysis, semiotic analysis, interpretative phenomenological analysis, and conversation analysis. Major quantitative content analyses include dictionary-based approaches, topic modeling, and natural language processing. Though specific steps for specific types of content analysis vary, a prototypical content analysis requires eight steps beginning with defining coding units and ending with assessing the trustworthiness, reliability, and validity of the overall coding. Furthermore, while most content analysis evaluates textual data, some studies also analyze visual data such as gestures, videos and pictures, and verbal data such as tone.
Content analysis has several advantages over other data collection and analysis methods. Content analysis provides a flexible set of tools that are suitable for many research questions where quantitative data are unavailable. Many forms of content analysis provide a replicable methodology to access individual and collective structures and processes. Moreover, content analysis of documents and videos that organizational actors produce in the normal course of their work provides unobtrusive ways to study sociocognitive concepts and processes in context, and thus avoids some of the most serious concerns associated with other commonly used methods. Content analysis requires significant researcher judgment such that inadvertent biasing of results is a common concern. On balance, content analysis is a promising activity for the rigorous exploration of many important but difficult-to-study issues that are not easily studied via other methods. For these reasons, content analysis is burgeoning in business and management research as researchers seek to study complex and subtle phenomena.
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Content and Text Analysis Methods for Organizational Research
Rhonda K. Reger and Paula A. Kincaid
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For-Purpose Enterprises and Hybrid Organizational Forms: Implications for Governance and Strategy
Marco S. Giarratana and Martina Pasquini
A company’s social purpose has become a key factor that should be considered in organizational design and strategic decision-making. For-purpose enterprises are for-profit, financially self-sustained organizations that embed a social aim as one of their main objectives. Companies that simultaneously must envisage a double purpose, namely, social and competitive, face an even greater complexity, that is, a likely risk of internal logics’ tensions and structural drifts.
Scholars have proposed different theoretical and operative frameworks; on the one hand, they describe ad hoc business models to foster synergies between the social impact and economic and competitive-oriented actions. On the other hand, they also try to focus on an organization’s governance, suggesting incentive schemes and organizational designs that could smooth trade-offs and tensions, which could jeopardize a company’s viability. Following scholars have differentiated two clusters of studies: (a) instrumental–strategic–economic stream and (b) injunctive–social–behavioral.
The first approach perceives as critical the balance between social-oriented aims and profit with a viable business model. Under this perspective, the concept of synergies between the two aims is critical. Its mainstream framework is the stakeholder theory approach while recent approaches, rooted especially in marketing and strategic human capital studies, bring to the central stage how corporate social responsible actions develop social identity processes with focal stakeholders, which are responsible for reciprocity behaviors. These different perspectives, although complementary, could imply significant differences for the organization design, product strategy, and the role and power of the chief sustainability officer as well as allocation of resources and capabilities.
The second group of studies—injunctive–social–behavioral—is focused on understanding how to maintain active social aims under economic and competitive constrains. These works are particularly focused in investigating the intrinsic motivations of doing good and the type of tensions that could arise in organizations with a social mission. The works analyze the potential drifts, risks, and solutions that could mitigate tension and trade-offs. In this stream, the first line of work is related to social entrepreneurship, especially in developing countries, while the second is more focused on human-resource incentive schemes and organizational designs that preserve a company’s social goals under economic constrains.
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Organizational Neuroscience
Sebastiano Massaro and Dorotea Baljević
Organizational neuroscience—a novel scholarly domain using neuroscience to inform management and organizational research, and vice versa—is flourishing. Still missing, however, is a comprehensive coverage of organizational neuroscience as a self-standing scientific field. A foundational account of the potential that neuroscience holds to advance management and organizational research is currently a gap. The gap can be addressed with a review of the main methods, systematizing the existing scholarly literature in the field including entrepreneurship, strategic management, and organizational behavior, among others.